<div class="csl-bib-body">
<div class="csl-entry">Rybinski, M., Kusa, W., Karimi, S., & Hanbury, A. (2024). Learning to match patients to clinical trials using large language models. <i>Journal of Biomedical Informatics</i>, <i>159</i>, Article 104734. https://doi.org/10.1016/j.jbi.2024.104734</div>
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dc.identifier.issn
1532-0464
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/208708
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dc.description.abstract
This study investigates the use of Large Language Models (LLMs) for matching patients to clinical trials (CTs) within an information retrieval pipeline. Our objective is to enhance the process of patient-trial matching by leveraging the semantic processing capabilities of LLMs, thereby improving the effectiveness of patient recruitment for clinical trials.
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dc.description.sponsorship
European Commission
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dc.language.iso
en
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dc.publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
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dc.relation.ispartof
Journal of Biomedical Informatics
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dc.subject
Humans
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dc.subject
Information Storage and Retrieval
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dc.subject
Semantics
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dc.subject
Algorithms
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dc.subject
Clinical trials
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dc.subject
Information retrieval
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dc.subject
Large language models
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dc.subject
Learning-to-rank
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dc.subject
Patient to trials matching
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dc.subject
TCRR
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dc.subject
TREC CT
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dc.subject
Clinical Trials as Topic
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dc.subject
Natural Language Processing
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dc.subject
Patient Selection
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dc.title
Learning to match patients to clinical trials using large language models
